Characterize the fetal kidney reference from the kidney cell atlas
Maud PLASCHKA
2024-08-07
Introduction
The aim is to characterize the human fetal kidney from the kidney
cell atlas. You can find more about the human kidney atlas here: https://www.kidneycellatlas.org/ [1] The rds data can be
download using the download link https://datasets.cellxgene.cziscience.com/40ebb8e4-1a25-4a33-b8ff-02d1156e4e9b.rds
The azimuth compatible reference has been downloaded and created in the
R script
download-and-create-fetal-kidney-ref.R
Packages
Load required packages in the following chunk, if needed. Do not
install packages here; only load them with the library()
function.
Base directories
Input files
The input file is the output of the R script
download-and-create-fetal-kidney-ref.R
Analysis
Load the reference
fetal_kidney <- readRDS(path_to_data)
d1 <- do_DimPlot(fetal_kidney, reduction = "umap", dims = c(1,2), group.by = "compartment", label = TRUE, repel = TRUE) + NoLegend()
d2 <- do_DimPlot(fetal_kidney, reduction = "umap", dims = c(1,2), group.by = "cell_type", label = TRUE, repel = TRUE) + NoLegend()
d1 | d2
Characterization of compartment and cell types in the reference
Here, we use an unbiased approach to find transcripts that characterized the different compartments and cell types.
This is just to get markers genes of the different population, in case some could be of interest for the Wilms tumor annotations.
We run DElegate::FindAllMarkers2 to find markers of the different clusters and manually check if they do make sense. DElegate::FindAllMarkers2 is an improved version of Seurat::FindAllMarkers based on pseudobulk differential expression method. Please check the preprint from Chistoph Hafemeister: https://www.biorxiv.org/content/10.1101/2023.03.28.534443v1 and tool described here: https://github.com/cancerbits/DElegate
Find marker genes for each of the compartment
## Warning in size + sum(size_args, na.rm = FALSE): NAs produced by integer
## overflow#filter the most relevant markers
s.markers <- de_results[de_results$padj < params$padj_threshold & de_results$log_fc > params$lfc_threshold & de_results$rate1 > params$rate1_threshold,]
DT::datatable(s.markers, caption = ("marker genes"),
extensions = 'Buttons',
options = list( dom = 'Bfrtip',
buttons = c( 'csv', 'excel')))# Select top 5 genes for heatmap plotting
s.markers <- na.omit(s.markers)
s.markers %>%
group_by(group1) %>%
top_n(n = 5, wt = log_fc) -> top5
# subset for plotting
Idents(fetal_kidney) <- fetal_kidney$compartment
cells <- WhichCells(fetal_kidney, downsample = 100)
ss <- subset(fetal_kidney, cells = cells)
ss <- ScaleData(ss, features = top5$feature)
p1 <- SCpubr::do_DimPlot(fetal_kidney, reduction="umap", group.by = "compartment", label = TRUE, repel = TRUE) + ggtitle("compartment")
p2 <- DoHeatmap(ss, features = top5$feature, cells = cells, group.by = "compartment") + NoLegend() +
scale_fill_gradientn(colors = c("#01665e","#35978f",'darkslategray3', "#f7f7f7", "#fee391","#fec44f","#F9AD03"))
p3 <- ggplot(fetal_kidney@meta.data, aes(compartment, fill = compartment)) + geom_bar() + NoLegend()
common_title <- sprintf("Unsupervised clustering %s, %d cells", fetal_kidney@meta.data$orig.ident[1], ncol(fetal_kidney))
show((((p1 / p3) + plot_layout(heights = c(3,2)) | p2) ) + plot_layout(widths = c(1, 2)) + plot_layout(heights = c(3,1)) + plot_annotation(title = common_title))
Find marker genes for each of the cell types
## Warning in size + sum(size_args, na.rm = FALSE): NAs produced by integer
## overflow#filter the most relevant markers
s.markers <- de_results[de_results$padj < params$padj_threshold & de_results$log_fc > params$lfc_threshold & de_results$rate1 > params$rate1_threshold,]
DT::datatable(s.markers, caption = ("marker genes"),
extensions = 'Buttons',
options = list( dom = 'Bfrtip',
buttons = c( 'csv', 'excel')))# Select top 5 genes for heatmap plotting
s.markers <- na.omit(s.markers)
s.markers %>%
group_by(group1) %>%
top_n(n = 5, wt = log_fc) -> top5
# subset for plotting
Idents(fetal_kidney) <- fetal_kidney$cell_type
cells <- WhichCells(fetal_kidney, downsample = 100)
ss <- subset(fetal_kidney, cells = cells)
ss <- ScaleData(ss, features = top5$feature)
p1 <- SCpubr::do_DimPlot(fetal_kidney, reduction="umap", group.by = "cell_type", label = TRUE, repel = TRUE) + ggtitle("cell_type") + NoLegend()
p2 <- DoHeatmap(ss, features = top5$feature, cells = cells, group.by = "cell_type") + NoLegend() +
scale_fill_gradientn(colors = c("#01665e","#35978f",'darkslategray3', "#f7f7f7", "#fee391","#fec44f","#F9AD03"))
p3 <- ggplot(fetal_kidney@meta.data, aes(cell_type, fill = cell_type)) + geom_bar() + NoLegend() + scale_x_discrete(guide = guide_axis(angle = 90))
common_title <- sprintf("Unsupervised clustering %s, %d cells", fetal_kidney@meta.data$orig.ident[1], ncol(fetal_kidney))
show((((p1 / p3) + plot_layout(heights = c(3,2)) | p2) ) + plot_layout(widths = c(1, 1)) + plot_layout(heights = c(3,1)) + plot_annotation(title = common_title))
References
Session info
## R version 4.4.1 (2024-06-14)
## Platform: aarch64-unknown-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
## BLAS: /usr/lib/aarch64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/aarch64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Etc/UTC
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] patchwork_1.2.0 lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1
## [5] dplyr_1.1.4 purrr_1.0.2 readr_2.1.5 tidyr_1.3.1
## [9] tibble_3.2.1 ggplot2_3.5.1 tidyverse_2.0.0 SCpubr_2.0.2
## [13] Azimuth_0.5.0 shinyBS_0.61.1 Seurat_5.1.0 SeuratObject_5.0.2
## [17] sp_2.1-4 optparse_1.7.5
##
## loaded via a namespace (and not attached):
## [1] fs_1.6.4 ProtGenerics_1.36.0
## [3] matrixStats_1.3.0 spatstat.sparse_3.1-0
## [5] bitops_1.0-8 DirichletMultinomial_1.46.0
## [7] TFBSTools_1.42.0 httr_1.4.7
## [9] RColorBrewer_1.1-3 tools_4.4.1
## [11] sctransform_0.4.1 utf8_1.2.4
## [13] R6_2.5.1 DT_0.33
## [15] lazyeval_0.2.2 uwot_0.2.2
## [17] rhdf5filters_1.16.0 withr_3.0.1
## [19] gridExtra_2.3 progressr_0.14.0
## [21] cli_3.6.3 Biobase_2.64.0
## [23] spatstat.explore_3.3-2 fastDummies_1.7.4
## [25] EnsDb.Hsapiens.v86_2.99.0 shinyjs_2.1.0
## [27] labeling_0.4.3 sass_0.4.9
## [29] spatstat.data_3.1-2 ggridges_0.5.6
## [31] pbapply_1.7-2 yulab.utils_0.1.7
## [33] Rsamtools_2.20.0 R.utils_2.12.3
## [35] parallelly_1.38.0 limma_3.60.4
## [37] BSgenome_1.72.0 rstudioapi_0.16.0
## [39] RSQLite_2.3.7 gridGraphics_0.5-1
## [41] generics_0.1.3 BiocIO_1.14.0
## [43] vroom_1.6.5 crosstalk_1.2.1
## [45] gtools_3.9.5 ica_1.0-3
## [47] spatstat.random_3.3-1 googlesheets4_1.1.1
## [49] GO.db_3.19.1 Matrix_1.7-0
## [51] fansi_1.0.6 S4Vectors_0.42.1
## [53] abind_1.4-5 R.methodsS3_1.8.2
## [55] lifecycle_1.0.4 edgeR_4.2.1
## [57] yaml_2.3.10 SummarizedExperiment_1.34.0
## [59] rhdf5_2.48.0 SparseArray_1.4.8
## [61] Rtsne_0.17 grid_4.4.1
## [63] blob_1.2.4 promises_1.3.0
## [65] shinydashboard_0.7.2 crayon_1.5.3
## [67] pwalign_1.0.0 miniUI_0.1.1.1
## [69] lattice_0.22-6 cowplot_1.1.3
## [71] GenomicFeatures_1.56.0 annotate_1.82.0
## [73] KEGGREST_1.44.1 pillar_1.9.0
## [75] knitr_1.48 GenomicRanges_1.56.1
## [77] rjson_0.2.22 future.apply_1.11.2
## [79] codetools_0.2-20 fastmatch_1.1-4
## [81] leiden_0.4.3.1 glue_1.7.0
## [83] spatstat.univar_3.0-0 data.table_1.16.0
## [85] vctrs_0.6.5 png_0.1-8
## [87] spam_2.10-0 cellranger_1.1.0
## [89] gtable_0.3.5 poweRlaw_0.80.0
## [91] assertthat_0.2.1 cachem_1.1.0
## [93] xfun_0.47 Signac_1.14.0
## [95] S4Arrays_1.4.1 mime_0.12
## [97] pracma_2.4.4 survival_3.7-0
## [99] DElegate_1.2.1 gargle_1.5.2
## [101] RcppRoll_0.3.1 statmod_1.5.0
## [103] fitdistrplus_1.2-1 ROCR_1.0-11
## [105] nlme_3.1-166 bit64_4.0.5
## [107] RcppAnnoy_0.0.22 GenomeInfoDb_1.40.1
## [109] rprojroot_2.0.4 bslib_0.8.0
## [111] irlba_2.3.5.1 KernSmooth_2.23-24
## [113] SeuratDisk_0.0.0.9021 colorspace_2.1-1
## [115] seqLogo_1.70.0 BiocGenerics_0.50.0
## [117] DBI_1.2.3 tidyselect_1.2.1
## [119] bit_4.0.5 compiler_4.4.1
## [121] curl_5.2.2 hdf5r_1.3.11
## [123] DelayedArray_0.30.1 plotly_4.10.4
## [125] rtracklayer_1.64.0 scales_1.3.0
## [127] caTools_1.18.2 lmtest_0.9-40
## [129] rappdirs_0.3.3 digest_0.6.37
## [131] goftest_1.2-3 presto_1.0.0
## [133] spatstat.utils_3.1-0 rmarkdown_2.28
## [135] XVector_0.44.0 htmltools_0.5.8.1
## [137] pkgconfig_2.0.3 sparseMatrixStats_1.16.0
## [139] MatrixGenerics_1.16.0 highr_0.11
## [141] fastmap_1.2.0 ensembldb_2.28.1
## [143] rlang_1.1.4 htmlwidgets_1.6.4
## [145] UCSC.utils_1.0.0 shiny_1.9.1
## [147] farver_2.1.2 jquerylib_0.1.4
## [149] zoo_1.8-12 jsonlite_1.8.8
## [151] BiocParallel_1.38.0 R.oo_1.26.0
## [153] RCurl_1.98-1.16 magrittr_2.0.3
## [155] ggplotify_0.1.2 GenomeInfoDbData_1.2.12
## [157] dotCall64_1.1-1 Rhdf5lib_1.26.0
## [159] munsell_0.5.1 Rcpp_1.0.13
## [161] viridis_0.6.5 reticulate_1.38.0
## [163] stringi_1.8.4 zlibbioc_1.50.0
## [165] MASS_7.3-61 plyr_1.8.9
## [167] parallel_4.4.1 listenv_0.9.1
## [169] ggrepel_0.9.5 deldir_2.0-4
## [171] CNEr_1.40.0 Biostrings_2.72.1
## [173] splines_4.4.1 tensor_1.5
## [175] hms_1.1.3 locfit_1.5-9.10
## [177] BSgenome.Hsapiens.UCSC.hg38_1.4.5 igraph_2.0.3
## [179] spatstat.geom_3.3-2 RcppHNSW_0.6.0
## [181] reshape2_1.4.4 stats4_4.4.1
## [183] TFMPvalue_0.0.9 XML_3.99-0.17
## [185] evaluate_0.24.0 JASPAR2020_0.99.10
## [187] tzdb_0.4.0 httpuv_1.6.15
## [189] RANN_2.6.2 getopt_1.20.4
## [191] polyclip_1.10-7 future_1.34.0
## [193] SeuratData_0.2.2.9001 scattermore_1.2
## [195] xtable_1.8-4 restfulr_0.0.15
## [197] AnnotationFilter_1.28.0 RSpectra_0.16-2
## [199] later_1.3.2 googledrive_2.1.1
## [201] viridisLite_0.4.2 memoise_2.0.1
## [203] AnnotationDbi_1.66.0 GenomicAlignments_1.40.0
## [205] IRanges_2.38.1 cluster_2.1.6
## [207] timechange_0.3.0 globals_0.16.3